Data literacy: What, Why

and How?

Data and Statistics: the sciences,

the literacies and collaborations

Helen MacGillivray

President-elect, International Statistical Institute Principal Fellow, Higher Education Academy

Australian Senior Learning and Teaching Fellow

Lessons from statistics

statistical literacy and statistical sciences What can be learnt for data literacy from decades of

promoting and efforts to enable statistical literacy?

What can be learnt for data science from experiences with statistical sciences?

Experiences from learning and teaching

Education across all levels Primary, junior secondary, upper secondary

Tertiary: other disciplines and training of future professionals

Workplace, professionals, researchers, managers, consultants

Adults, society

Education is both the challenge and the key

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Lessons from statistics

- statistical literacy and statistical sciences Some initial advice

Descriptions can be constructive but definitions are not

Discussion essential and enlightening but diagrammatic representations are not

Definitions and diagrammatic representations tend to take time and attention aware from productive effort and impose misleading and unnecessary boundaries

(For example, anything involving Venn diagrams a waste of space)

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Some descriptions of statistical literacy Statistical literacy is necessary for citizens to understand

material presented in publications such as newspapers, television and the internet.

Good “statistical citizens”: able to consume the information

that they are inundated with on a daily basis, think critically

about it, and make good decisions based on that

information. Some researchers call this “statistical literacy.”

Rumsey (2002)

People’s ability to interpret and critically evaluate statistical

information and data-based arguments appearing in diverse

media channels, and their ability to discuss their opinions

regarding such statistical information (Gal 2000)

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Some descriptions of statistical literacy A simple example from school levels

Upper primary: read the data

Junior secondary: read between the data

Middle/senior secondary: read beyond the data

The University of …… statistical literacy programme

Module 1. Producing data

Module 2. Describing, Clarifying and Presenting Data

Module 3. Interpreting data

Completing these modules will help you to develop the skills you

need to:

look behind the data with which you are presented at University and

in your everyday experiences,

ask why these data are being presented in those forms,

ask what questions can be answered or what arguments are being

made with these data.

As you work through these modules you should become much

more critical about the way data is produced, the way data is

presented and the way data is interpreted. 5

Some descriptions of data literacy Data literacy is the ability to read, create and

communicate data as information and has been formally described in varying ways.

The desire and ability to constructively engage in society through and about data http://datapopalliance.org/item/what-is-data-literacy/

http://databrarians.org/2015/02/what-is-data-literacy/

In Libraryland, “data literacy” seems to consist of two aspects: information literacy and data management.

Data literacy is the ability to interpret, evaluate, and communicate statistical information…how statistical information is created, encompassing data production

Data management …. belongs to the data production phase … perhaps one aspect of data literacy that can be reserved for the specialists.

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The what

Descriptions constructive, definitions not

Discussion of inter-relationships constructive because different descriptions help everyone understand what and why

Any subset attempts or representations misleading and waste of time.

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The why Descriptions give reasons:

for everyone to extent appropriate for level of education,

for training and work context

The how

RSS Centre for

Statistical Education

problem-solving cycle

Plan

Collect data

Process data

Discuss

Popularised by Wild and

Pfannkuch (1999). From

quality/industrial statistics

MacKay and Oldfield

(1994), Shewhart and

Deming (1986)

Based on data-handling

cycle UK National School

Curriculum 1970’s

(Holmes, 1997). Problem-

solving cycle (Marriott,

Davies and Gibson, 2009) 8

Statistical investigation process (under various terms, descriptions)

heart of statistical education and practice of statistics

From statisticians

Cameron (2009) considers

desirable key components of university-based training

consults what many “wide and experienced” statisticians have written (e.g. Box, 1976)

builds on Chambers’ (1993) “greater statistics”

identifies formulating a problem so that it can be tackled statistically

preparing data (including planning, collecting, organising and validating)

analysing data

presenting information from data

researching the interplay of observation, experiment and theory.

comments that such training is an appropriate foundation for most statisticians wherever they may be employed.

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From statisticians Kenett & Thyregod (2005) describe the 5 steps in statistical consulting

problem elicitation

data collection and/or aggregation

data analysis using statistical methods

formulation of findings & consequences

presentation of findings and conclusions/recommendations.

“important to take part in collection of data, or at least have

the opportunity to watch data being collected or generated.”

and that not being involved in collecting data

“has led some graduates to be of the opinion that taking part

in the collection of data is a waste of the statistician’s

precious time...implies risk of getting dirt on your hands.”

“Our long-term objective is to encourage academic courses

to cover the full 1–5 cycle....especially steps 1, 2 and 5” 10

Statistical investigating at the heart of the

discipline, science and profession of statistics

Barnett (1986)

We see, tied up together, the role of the statistician as consultant, consultancy as the stimulus for research in statistics, and consultancy as the basis for teaching statistics

Bisgaard and Bisgaard (2005) outline 3 roles: a pair of hands; the expert; the catalyst/collaborator/coach

Cameron (2009): ‘entwined collaboration’ and ‘serial collaboration’

The practice of statistics – from statisticians

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Note advocacy for no division at introductory tertiary; same foundation. For example, Wild (2006), MacGillivray

(1998, 2005a), Cameron (2009)

The how

Some great work internationally, nationally and locally, but

Penetration not great and problems persist. Why? • Nature, pervasiveness of statistics; universality of

educational needs

• Dynamic nature of statistics: responds to data, technologies, disciplines, workplaces

• Far too much of new ways of teaching old; other disciplines

• Technology resources

• Not enough real, complex, many-variable datasets

• Cobb (2015): Mere renovation is too little too late: we need to rethink our undergraduate curriculum from the ground up

Penetration and implementation of these decades of advocacy for statistical education for all and for professional training?

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Understanding the what of statistics

Some impediments: belief in other disciplines that can add to basic background; calculations; trickle down effects from research

Some impediments for data literacy and data science? Similar – for ‘calculations’ substitute ‘coding’

• Statistics is the science of data, variation and uncertainty.

• Statistics sources, evaluates, appraises, interrogates, investigates, models, critiques, develops, applies, interprets and communicates data, variation and the information therein.

• Statistics works with, within and across all disciplines, government, business, industry and society.

• Statistics and statistical thinking are pervasive, universal and central to all evidence-based progress and furthering of knowledge.

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The how and collaboration Professionals need to get involved in the nitty gritty

Observe, listen, communicate

Enable coherent development over school

Authentic working with other disciplines

ASSESSMENT is key Simple to complex

Real contexts, real data, complex data

Technology resources for learning and assessment

Look for cross-overs not boundaries

Collaboration & sharing

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Thank you & here’s to

statistics and data

Strengthening the ‘roots’ in undergraduate

curricula for future statisticians

Heed long-time advocacy of professional statisticians Barnett (1986)

“we see, tied up together, the role of the statistician as consultant, consultancy as the stimulus for research in statistics, and consultancy as the basis for teaching statistics”.

Authentic experience of full statistical investigation process:

Cameron (2009) builds on Chambers’ (1993) ‘greater statistics’

Kenett & Thyregod (2005) also describe 5 similar steps in statistical practice/consulting “important to take part in collection of data, or at least have the

opportunity to watch data being collected or generated.”

“encourage academic courses to cover the full 1–5

cycle....especially steps 1, 2 and 5.”

Real, large contexts and data: simple within complex

Maths as servant of statistics

Technological and data systems know-how 15

Strengthening roots in school curricula

Analyse what’s gone wrong in statistical education ‘reform’ over 2-3 decades New dogma for old

New ways of learning old content & old sequencing e.g. inference for means before proportions

Domination of 1 and 2 variables and measures

Toy datasets

Lack of coherent development

Domination of psychology thinking e.g. analysing understanding of sampling distributions

‘The’ question & ‘the’ answer

Need Variables, variation, visualisation

Coherent development built up around types of variables

Authentic full statistical data investigations – from simplest

Simple within complex

16 Thank you and here’s to statistics!